Remaining useful life prognostics for aeroengine based on superstatistics and information fusion  

作　　者：Liu Junqiang Zhang Malan Zuo Hongfu Xie Jiwei 

机构地区：[1]College of Civil Aviation, Nanjing University of Aeronautics and Astronautics

出　　处：《Chinese Journal of Aeronautics》2014年第5期1086-1096,共11页中国航空学报（英文版）

基　　金：co-supported by the State Key Program of National Natural Science of China (No. 61232002);the Joint Funds of the National Natural Science Foundation of China (No. 60939003);China Postdoctoral Science Foundation (Nos. 2012M521081, 2013T60537);the Fundamental Research Funds for the Central Universities of China (No. NS2014066);Postdoctoral Science Foundation of Jiangsu Province of China (No. 1301107C);Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu of China (No. 2014SJD041)

摘　　要：Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.

关 键 词：Degradation Information fusion Kalman filtering Performance Prognostics Remaining useful life Superstatistics 

分 类 号：V263.6[航空宇航科学与技术—航空宇航制造工程]